Adaptive Update Handling for Graph HTAP

Processing hybrid transactional/analytical workloads on graph data can significantly benefit from GPU accelerators. However, to exploit the full potential of GPU processing, dedicated graph representations are necessary, which make inplace updates difficult. In this paper, we discuss an approach for adaptive handling of updates in a graph database system for HTAP workloads. We discuss and evaluate strategies for propagating updates from an update-friendly table storage to a GPU-optimized sparse matrix format.